Parkinson's Disease Predictions Possible From Blood Plasma Proteins, Study Finds

Original from: genomeweb

With the help of proteomic profiling and machine learning, an international team led by investigators in the UK and Germany has identified a handful of blood plasma proteins associated with subsequent Parkinson's disease development, pointing to the possibility of developing blood-based biomarkers for the progressive neurodegenerative condition.

"This specific blood panel indicates molecular events in early stages and could help identify at-risk participants for clinical trials aimed at slowing/preventing motor Parkinson's disease," co-first and co-corresponding authors Jenny Hällqvist, with University College London, and Michael Bartl, at University Medical Center Goettingen, and their colleagues wrote in Nature Communications on Tuesday.

Past studies have demonstrated that Parkinson's disease symptoms ranging from movement and memory changes to tremors stem from an alpha-synuclein buildup leading to nerve cell degradation and altered dopamine production in the substantia nigra, a brain region involved in movement.

While conventional Parkinson's disease treatment typically involves dopamine replacement treatment in individuals who are already experiencing such symptoms, the team reasoned that the ability to predict onset of the disease and treat it at an earlier stage may help to slow the insidious advance of the disease.

"At present we are shutting the stable door after the horse has bolted, and we need to start experimental treatments before patients develop symptoms," co-senior author Kevin Mills, a researcher with the UCL Queen Square Institute of Neurology, said in a statement. "Therefore, we set out to use state-of-the-art technology to find new and better biomarkers for Parkinson's disease and develop them into a test that we can translate into any large [UK National Health Service] laboratory."

Using quadrupole time-of-flight mass spectrometry, the team profiled blood plasma proteins in samples from 10 individuals with treatment-naïve Parkinson's disease and 10 unaffected control individuals. In the process, they identified a Parkinson's disease-associated shift in plasma levels of eight proteins: granulin precursor; mannan binding lectin serine peptidase 2; endoplasmic reticulum chaperone BiP; prostaglaindin-H2 D-isomaerase; intercellular adhesion molecule-1; complement C3; Dickkopf-WNT-signaling pathway inhibitor-3; and plasma protease C1 inhibitor.

"[I]nstead of single biomarkers, in a univariate approach, we have created a pipeline using a targeted proteomic test of a multiplexed panel of proteins, together with machine learning," the authors wrote, adding that the "powerful combination of multiple well-selected biomarkers with state-of-the-art machine-learning bioinformatics, allowed us to use a panel of eight biomarkers that could distinguish early PD from [healthy controls]."

The researchers used targeted mass spec to validate the proposed biomarkers with data for another 99 individuals recently diagnosed with Parkinson's disease, along with three dozen unaffected control individuals and 72 individuals with a related sleep condition but no motor symptoms, bringing in machine learning analyses to explore their predictive capabilities.

The team also saw comparable blood plasma protein shifts in more than three-quarters of the 72 individuals with isolated REM sleep behavior disorder (iRBD), a sleep condition marked by intense dreams and physical movements that can precede Parkinson's disease and other synucleinopathy conditions involving alpha-synuclein buildup in the brain.

Moreover, the researchers' results from 16 participants ultimately diagnosed with Parkinson's disease suggested that the blood plasma protein shifts can start turning up as long as seven years before disease development.

"We have not only developed a test, but can diagnose the disease based on markers that are directly linked to processes such as inflammation and degradation of non-functional proteins," co-first author Bartl said in a statement, noting that the newly detected markers "represent possible targets for new drug treatments."

Members of the team have now set out to secure funding to continue developing the potential Parkinson's biomarkers with the aim of coming up with a more widely used test for the condition in the coming years.

In addition, the authors explained, the proposed set of biomarkers "provided a distinct signature of protective and detrimental mechanisms" long before motor symptoms appeared, highlighting oxidative stress and neuroinflammation roles in alpha-synuclein buildup, which leads to so-called Lewy body formation.

"[T]his blood panel can, in the future, upon further validation help identify subjects at risk of developing [Parkinson's disease/dementia with Lewy bodies] and stratify them for upcoming prevention trials," they suggested.

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Source: Parkinson's Disease Predictions Possible From Blood Plasma Proteins, Study Finds